Estimating latent infections

A retrospective


Ryan J. Tibshirani

Daniel J. McDonald, Rachel Lobay, and CMU’s Delphi Group

CDC Flu – 18 December 2023

Goal

Use reported cases to estimate actual infections

  • For every US state
  • Between March 1, 2020 to January 1, 2022
  • Provide an “authoritative” estimate with uncertainty
  • No compartmental models, no sampling or Bayesian methods

Retrospective deconvolution

  • Based on prior work (Jahja et al. 2021)
  • Take reported cases and deconvolve them to find when symptoms began
  • Private CDC linelist to estimate the delay from symptom onset to case report
    • Different delay distribution for every report date and state
  • Combine with Literature estimate of the delay from infection to symptom onset
    • Variant specific
    • Prevailing variant mix taken from GISAID
  • Convolve both to get delay distribution from infection to case report

Empirical delay distributions

need this data.

Variant mix

Need this data.

Incubation period by state

Convolved distribution – Infection to case report

Cases are selectively reported to CDC

  • CDC linelist with both onset and report date
  • Shrink the parameters to national proportionally

Some deconvolution math

Deconvolve cases by their delay distribution

Estimate the inverse reporting ratio

Serology data

State space model

Results

Callouts

Final slide

Thanks:

  • The whole CMU Delphi Team (across many institutions)
  • Optum/UnitedHealthcare, Change Healthcare.
  • Google, Facebook, Amazon Web Services.
  • Quidel, SafeGraph, Qualtrics.
  • Centers for Disease Control and Prevention.
  • Council of State and Territorial Epidemiologists